TSRuleGrowth : Extraction de règles de prédiction semi-ordonnées à partir d'une série temporelle d'éléments discrets, application dans un contexte d'intelligence ambiante

Abstract : This paper presents a new algorithm : TSRuleGrowth, looking for partially-ordered rules over a time series. This algorithm takes principles from the state of the art of rule mining and applies them to time series via a new notion of support. We apply this algorithm to real data from a connected environment, which extract user habits through different connected objects.
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https://hal.archives-ouvertes.fr/hal-02190737
Contributor : Benoit Vuillemin <>
Submitted on : Tuesday, July 23, 2019 - 11:34:29 AM
Last modification on : Thursday, November 21, 2019 - 2:17:28 AM

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  • HAL Id : hal-02190737, version 2

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Benoit Vuillemin, Lionel Delphin-Poulat, Rozenn Nicol, Laëtitia Matignon, Salima Hassas. TSRuleGrowth : Extraction de règles de prédiction semi-ordonnées à partir d'une série temporelle d'éléments discrets, application dans un contexte d'intelligence ambiante. Conférence Nationale sur les Applications Pratiques de l'Intelligence Artificielle (APIA), Jul 2019, Toulouse, France. pp.82-89. ⟨hal-02190737v2⟩

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